Making the telematics numbers add up

In many ways, the situation right now in the connected car space is a lot like priming a pump just before the flow starts gushing out. The number of connected vehicles on the road is growing rapidly but still hasn’t reached the tipping point.

ADAS, keeps inching closer and closer towards being fully autonomous but the near horizon for non-experimental deployment of driverless cars remains five years out. Ride- and car-sharing services, such as Uber and Lyft are becoming more widely accepted with each passing week but, again, there are still plenty of hurdles, mostly bureaucratic ones, to clear before they truly make their mark outside a number of select metropolitan areas like Seattle, San Francisco, Austin, Washington, and Brooklyn. For one thing, connected- and fully driverless cars won’t really come into their own until vehicle-to-vehicle and vehicle-to-infrastructure communications systems are up and in place on the nation’s roads and highways. This still has several years to go before either is happening but, even if none of it has quite happened yet, there is no question that it will. Nothing short of a global catastrophe will keep the Connected-Car Revolution from happening.

Once the vehicle-to-vehicle and vehicle-to-infrastructure networks are in place and connected- and driverless cars have become the rule and not the exception, significant revenue streams will also begin flowing in many directions to different points along the value chain. While this will no doubt broaden in time, for the near-term, monetization will come in three basic areas: data gathering and re-utilization, information and infotainment services, and connected-car commerce

Data

It’s estimated that currently more data is generated worldwide every two days than had been created up until 2003. As the IoT comes together, linking billions of machines, all of them busily contributing their bit to the data mass, this amount will only continue growing exponentially. No small amount of it will come from connected cars, which represents the fastest growing segment of the IoT market. Today, data starts being generated and transmitted from connected cars the moment their ignition is switched on. Right now most of what gets generated is engineering data, being collected by the OEM mainly for its own benefit. They use it primarily to improve their vehicles and to learn what is working optimally and what is not. Increasingly, though, what is being generated is behavioural data about how the vehicle is being operated as well the conditions and environment within which it is operating. In a way, it represents the beginning of the new data sharing paradigm and the disruption that will come with it. Currently the people interested in it are mainly the insurance companies.

Until now, insurance companies depended on two things to assess drivers for insurance; their driving record and actuarial data. While the driver’s official record might or might not provide an accurate listing of accidents and moving violations, the actuarial information didn’t reflect their actual driving behaviour at all. What it did was assemble a risk profile factoring in what would be likely statistically for someone of the driver’s age, gender, who’d reached the same educational level, kept jobs for the same amount of time and had the same credit history. Even though none of it had anything to do with how they drove, from this and a hundred years’ worth of actuarial data, insurers could surmise, usually very accurately, what their actual driving behaviour would be like, and charge them for premiums accordingly. While this has worked well enough, it is now possible for insurers to assess risk based on real information about their driving behaviour.

By using a recording device known as a ‘dongle,’ attached to the driver’s vehicle’s OBD port, an insurer can find out what their actual driving behaviour is like; what hours they drive, whether they drive too fast, how often they change lanes, and how often they do hard braking. Using this actual performance data, insurers can offer good drivers a better premium, without regard to whether or not they finished graduate school. Continued monitoring will reveal whether their driving behaviours have improved, which is particularly important when assessing both teenage and older drivers.

Of course, this sort of data gathering is not limited to dongles and OBD ports. Smartphones with the right downloaded apps can gather the same information, as can, for that matter, the vehicle’s telematics rig. More importantly, they can also be gathering information about vehicle location, weather and road conditions. This is critical, because, in order for performance-based insurance to fully work, for a country like, for example, Canada, something like a billion miles of vehicle driving data must first be gathered and analysed. Until they can gather their own billion miles of driving data, Canadian insurers have actually had to purchase analogous data from other, non-Canadian, sources. In a sense, this may mark the creation of the first telematics data-derived revenue stream. In time, that ‘filler’ data will get replaced by locally-produced road data and as it grows, it will start having value on its own, not just to the insurance industry but to third parties as well.

By itself, the driver data that comes out of a dongle might be sufficient for an insurance provider to write up a premium for a driver but, generally speaking, it’s not good for much else, since that data lacks any operational context. But when the data is contextualised and capable of having analytic tools applied to it, road data becomes extremely valuable, particularly to cities, counties and regional transportation authorities. From it, they can discern road use patterns, they can figure out where the hazardous stretches of road are and the environmental circumstances where they present the most risk. Analysed differently, road data can also reveal valuable insights of great interest to roadside businesses, such as which roadside business locations draw the most traffic and from how far away.

In the near future, a new data source will be added; driver behavioural data. David Dimeo, director of connected car innovation for Ford Direct, a joint venture between Ford and Ford-Lincoln dealerships, describes it as “the data that tells how you’re using that product, where you’re using that product and what the features are that interest you the most. Is it beneficial for the OEM to know that 30% of their feature mix is rarely used by the customer? Having access to that kind of data tells the OEMs how to save costs in reinvest elsewhere”. Dimeo says that data would also benefit the driver because “having that data about the driver’s usage, you can come back with an offer tailored to their needs. They might start seeing the relationship between car buying and car ownership as less adversarial”.

Having access into real-time vehicle performance allows dealerships and OEMs to provide both proactive and predictive customer service, which in turn drives more service revenue at the dealership level.

The big issue is with big data generated by individual drivers is privacy. In many countries, an individual driver’s data may not be incorporated into the vast sea of big data, unless they have given their express permission allowing it. Many countries, such as Canada, and numerous European ones, have strict privacy rules. But at the same time, surveys show that most drivers don’t have a problem trading in some personal information in exchange for discounts and reductions in what they have to pay for premiums. Trust is probably more important than privacy. As long as the driver remains confident that their personal information does not get leaked out or misused, they’ll be happy to let the OEMs or TSPs have access to their personal information, especially if it means cheaper services or a reduced premium.

To everyone in the industry, the real issue isn’t data privacy but data ownership. “We estimate that the global revenue stream from connected cars will jump from $16Bn (£12.2Bn) in 2013 to $40Bn by 2018, so perhaps more attention should be given to asking how much the OEMs, TSPs, or MNOs might be willing to share the projected revenues from the data their systems will generate,” says Freidmar Rumpel, vice president for Alix Partners automotive practice.

Turning raw data into actionable information is an act of value creation which the creator is understandably loathe to openly share. Last year Renault and Nissan partnered up with The Floow, a well-known provider of telematics data services to the insurance industry, in an effort to monetize OEM vehicle data. The Floow will deliver an analytics suite using original data from current and future generations of Nissan-Renault vehicles. Presumably other OEMs will follow similar strategies to turn raw automotive data into actionable, moneymaking insights that belong only to them.

“There are many examples out in the marketplace today that give us a hint at how data ownership is going to be handled,” says Bryan Biniak, entrepreneur in residence at Nokia Growth Partners. “If you look at what the telecommunications companies have done with the smartphone and what the relationship is between the telco and the consumer and who owns what. Once you begin to parse the data, in exchange for it the customer is going to want some no-cost services and shared revenue services.”

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